Amazon SageMaker and SageMaker inference endpoints provide a capability of training and deploying your AI and machine learning (ML) workloads. With inference endpoints, you can deploy your models for real-time or batch inference. The endpoints support various types of ML models hosted using AWS Deep Learning Containers or your own containers with custom AI/ML algorithms. When you launch SageMaker inference endpoints with multiple instances, SageMaker distributes the instances across multiple Availability Zones (in a single Region) for high availability.
In some cases, however, to ensure lowest possible latency for customers in diverse geographical areas, you may require deploying inference endpoints in multiple Regions. Multi-Regional deployment of SageMaker endpoints and other related application and infrastructure components can also be part of a disaster recovery strategy for your mission-critical workloads aimed at mitigating the risk of a Regional failure.
SageMaker Projects implements a set of pre-built MLOps templates that can help manage

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